title: Identifying crash type propensity using real-time traffic data on freeways.
abstract: BACKGROUND
  We examine the effects of various traffic parameters on type of road crash.
  
  
  METHODS
  Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France.
  
  
  RESULTS
  Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent.
  
  
  CONCLUSIONS
  Results could be integrated in a real-time traffic management application.
authors.affiliation: Université Paris-Est, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), «Le Descartes 2», 2 Rue de la Butte Verte, 93166 Noisy-Le-Grand, France. zoic@civil.ntua.gr
authors.forename: Zoi
authors.initials: Z
authors.lastname: Christoforou
authors.forename: Simon
authors.initials: S
authors.lastname: Cohen
authors.forename: Matthew G
authors.initials: MG
authors.lastname: Karlaftis
mesh_terms: D000063:Accidents, Traffic / Q000145:classification / Q000706:statistics & numerical data*; D001336:Automobiles / Q000706:statistics & numerical data*; D005602:France; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D057216:Propensity Score; D011634:Public Health; D036301:Qualitative Research; D013997:Time Factors